Consumer Electronics Research Articles

Wearable Near-Eye Tracking Technologies for Health: A Review.

With the rapid advancement of computer vision, machine learning, and consumer electronics, eye tracking has emerged as a topic of increasing interest in recent years. It plays a key role across diverse domains including human-computer interaction, virtual reality, and clinical and healthcare applications. Near-eye tracking (NET) has recently been developed to possess encouraging features such as wearability, affordability, and interactivity. These features have drawn considerable attention in the health domain, as NET provides accessible solutions for long-term and continuous health monitoring and a comfortable and interactive user interface. Herein, this work offers an inaugural concise review of NET for health, encompassing approximately 70 related articles published over the past two decades and supplemented by an in-depth examination of 30 literatures from the preceding five years. This paper provides a concise analysis of health-related NET technologies from aspects of technical specifications, data processing workflows, and the practical advantages and limitations. In addition, the specific applications of NET are introduced and compared, revealing that NET is fairly influencing our lives and providing significant convenience in daily routines. Lastly, we summarize the current outcomes of NET and highlight the limitations.

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Organizational Innovativeness: The Role of Innovation Adoption Capability

Organizations develop capabilities to manage activities and generate value for their customers. This study focuses on the adoption capability of innovation adopting organizations. Adoption capability is a complex, and multidimensional concept that includes a high level of uncertainty, making it difficult to measure. This study attempts to find a systematic methodology to measure the innovation adoption capability of firms, through which management can monitor and evaluate a firm’s strengths and weaknesses in each capability dimension compared with competitors, predict their future needs for improvements, and plan necessary activities to stay competitive in the market. The proposed model considers multiple quantitative and qualitative adoption capability dimensions simultaneously and utilizes a relatively comprehensive set of adoption capability elements by developing 35 items and classifying them into six dimensions. This study uses the fuzzy weighted aggregated sum product assessment (WASPAS) method as a multi-attribute decision-making (MADM) approach. The model is applied to three competing firms in the consumer electronics sector, and the results are discussed.

Electrode materials for calcium batteries: Future directions and perspectives

AbstractDespite the prevailing dominance of lithium‐ion batteries in consumer electronics and electric vehicle markets, the growing apprehension over lithium availability has ignited a quest for alternative high‐energy‐density electrochemical energy storage systems. Rechargeable batteries featuring calcium (Ca) metal as negative electrodes (anodes) present compelling prospects, promising notable advantages in energy density, cost‐effectiveness, and safety. However, unlocking the full potential of rechargeable Ca metal batteries particularly hinges upon the strategic identification or design of high‐energy‐density positive electrode (cathode) materials. This imperative task demands expeditious synthetic routes tailored for their meticulous design. In this Perspective, we mainly highlight the development in the cathode materials for calcium batteries and accentuate the unparalleled promise of solid‐state metathesis routes in designing a diverse array of high‐performance electrode materials.

Advanced Manufacturing Transformation: A Strategic Framework for the Smart Factory

This paper presents a comprehensive strategy for implementing advanced manufacturing operations within a Smart Factory framework for a large consumer electronics manufacturer. By leveraging digital twin technology, additive manufacturing, robotics, automation, and advanced materials, this strategy aims to enhance operational efficiency, implement advanced analytics, integrate smart technologies, and sustain competitive advantage. The approach includes ecosystem partner identification, technology integration, process optimization, data management, continuous improvement, governance, and compliance.

Carbon Micro/Nano Machining toward Miniaturized Device: Structural Engineering, Large-Scale Fabrication, and Performance Optimization.

With the rapid development of micro/nano machining, there is an elevated demand for high-performance microdevices withhigh reliability and low cost. Due to their outstanding electrochemical, optical, electrical, and mechanical performance, carbon materials are extensively utilized in constructing microdevices for energy storage, sensing, and optoelectronics. Carbon micro/nano machining is fundamental in carbon-based intelligent microelectronics, multifunctional integrated microsystems, high-reliability portable/wearable consumer electronics, and portable medical diagnostic systems. Despite numerous reviews on carbon materials, a comprehensive overview is lacking that systematically encapsulates the development of high-performance microdevices based on carbon micro/nano structures, from structural design to manufacturing strategies and specific applications. This review focuses on the latest progress in carbon micro/nano machining toward miniaturized device, including structural engineering, large-scale fabrication, and performance optimization. Especially, the review targets an in-depth evaluation of carbon-based micro energy storage devices, microsensors, microactuators, miniaturized photoresponsive and electromagnetic interference shielding devices. Moreover, it highlights the challenges and opportunities in the large-scale manufacturing of carbon-based microdevices, aiming to spark further exciting research directions and application prospectives.

A closer look at lithium-ion batteries in E-waste and the potential for a universal hydrometallurgical recycling process

The demand for lithium-ion batteries (LiBs) is rising, resulting in a growing need to recycle the critical raw materials (CRMs) which they contain. Typically, all spent LiBs from consumer electronics end up in a single waste stream that is processed to produce black mass (BM) for further recovery. It is desired to design a recycling process that can deal with a mixture of LiBs. Hence, this study investigates the structure and composition of battery modules in common appliances such as laptops, power banks, smart watches, wireless earphones and mobile phones. The battery cells in the module were disassembled into cell casing, cathode, anode and separator. Then, the cathode active materials (CAMs) were characterized in detail with XRD-, SEM-, EDX- and ICP-OES-analysis. No direct link was found between the chemistry of the active materials (NMC, LCO, LMO, LFP etc.) and the application. Various BM samples were submitted to a leaching procedure (2 M H2SO4, 50 °C, 2 h, 60 g BM/L) with varying concentration (0–4 vol%) of H2O2 to study the influence of their chemical composition on the dissolution of Li, Ni, Mn and Co. Only a part of the BMs dissolved completely at 4 vol% H2O2, which was attributed to the oxidation state of the transition metals (TMs). Exact determination of H2O2 consumption by redox titration confirmed this hypothesis.

A ±0.15 °C (3σ) Inaccuracy CMOS Smart Temperature Sensor from −40 °C to 125 °C with a 10 ms Conversion Time-Leveraging an Adaptative Decimation Filter in 65 nm CMOS Technology

This paper presents the design and implementation of a highly accurate smart temperature sensor designed in 65 nm CMOS technology. The sensor exhibits a ±0.15 °C (3σ) error across a wide temperature range from −40 °C to 125 °C, catering to diverse application needs. Leveraging advanced CMOS technology, the sensor employs an adaptive decimation filter that allows us to control the conversion time, ensuring that the accuracy of the conversion is maintained even in challenging conditions. The proposed sensor architecture integrates advanced techniques for temperature sensing for improved accuracy and reliability. Through meticulous circuit design and the usage of dynamic element matching, chopping, and calibration/trimming, the sensor demonstrates exceptional performance characteristics, making it suitable for various industrial, automotive, and consumer electronics applications demanding high precision temperature monitoring.

Integration of High‐Tc Superconductors with High‐Q‐Factor Oxide Mechanical Resonators

AbstractMicro‐mechanical resonators are building blocks of a variety of applications in basic science and consumer electronics. This device technology is mainly based on well‐established and reproducible silicon‐based fabrication processes with outstanding performances in term of mechanical Q‐factor and sensitivity to external perturbations. Broadening the functionalities of micro‐electro‐mechanical systems (MEMS) by the integration of functional materials is a key step for both applied and fundamental science. However, combining functional materials with silicon‐based devices is challenging. An alternative approach is directly fabricating MEMS based on compounds inherently showing non‐trivial functional properties, such as transition metal oxides. Here, a full‐oxide approach is reported, where a high‐ superconductor YBa2Cu3O7 (YBCO) is integrated with high Q‐factor micro‐bridge resonators made of single‐crystal LaAlO3 (LAO) thin films. LAO resonators are tensile strained, with a stress of about 350 MPa, show a Q‐factor above 200k, and have low roughness. YBCO overlayers are grown ex situ by pulsed laser deposition and YBCO/LAO bridges show zero resistance below 78 K and mechanical properties similar to those of bare LAO resonators. These results open new possibilities toward the development of advanced transducers, such as bolometers or magnetic field detectors, as well as experiments in solid state physics, material science, and quantum opto‐mechanics.

Self-Assembled Molecular Layers as Interfacial Engineering Nanomaterials in Rechargeable Battery Applications.

Rechargeable batteries have transformed human lives and modern industry, ushering in new technological advancements such as mobile consumer electronics and electric vehicles. However, to fulfill escalating demands, it is crucial to address several critical issues including energy density, production cost, cycle life and durability, temperature sensitivity, and safety concerns is imperative. Recent research has shed light on the intricate relationship between these challenges and the chemical processes occurring at the electrode-electrolyte interface. Consequently, a novel approach has emerged, utilizing self-assembled molecular layers (SAMLs) of meticulously designed molecules as nanomaterials for interface engineering. This research provides a comprehensive overview of recent studies underscoring the significant roles played by SAML in rechargeable battery applications. It discusses the mechanisms and advantageous features arising from the incorporation of SAML. Moreover, it delineates the remaining challenges in SAML-based rechargeable battery research and technology, while also outlining future perspectives.

Cultivating Brand Loyalty through Content Marketing: The influence of Digital Content Characteristics on Loyalty towards Consumer Electronic Brands

The research paper explores the impact of digital content marketing on brand loyalty towards consumer electronic brands. It examines whether content characteristics: type, quality, and platform influence consumer loyalty towards electronic brands. The study uses a questionnaire of 72 respondents and employs Structural Equation Modelling (SEM) for testing the effect of content characteristics on brand loyalty among consumers. The findings suggest that content platform and quality has a significant positive influence on brand loyalty. However, no significant effect of content type was found on the brand loyalty. The study recommends that consumer electronics brands should focus on selecting the right platforms and creating high quality content to enhance brand loyalty. Future research could explore additional content characteristics that may potentially influence brand loyalty. The study is useful for consumer electronic brands, marketers and researchers who seek to gather and develop insights pertaining to the enhancement of their content marketing strategies to yield better marketing outcomes.

Integration of Principal Component Analysis with AHP-QFD for Improved Product Design Decision-Making

The complexity of quality function deployment (QFD) matrices often hinders efficient decision-making in product design, leading to missed opportunities and extended development times. This study explores the integration of principal component analysis (PCA) with analytic hierarchy process-QFD (AHP-QFD) to address these challenges. PCA, a machine learning technique, was applied to QFD matrices from product design research to reduce complexity and enhance prioritization efficiency. The integrated method was tested with a product design team across various industries, including logistics, healthcare, and consumer electronics. The analysis demonstrated that PCA effectively reduced matrix complexity, optimizing feature prioritization. In the logistics sector, PCA explained 99.2% of the variance with the first five components, while in consumer electronics, it accounted for 86.9% with the first four components. However, PCA showed limitations in the healthcare sector due to evenly distributed variance among components. Expert feedback highlighted the practical benefits of the integrated approach: 75% of logistics experts and 62.5% of consumer electronics experts found the method clearer. For speed, 100% of logistics and 87.5% of consumer electronics experts preferred the method for quicker evaluations. For accuracy, 75% of logistics and 62.5% of consumer electronics experts deemed the method more accurate. Overall, the PCA-AHP-QFD method simplifies decision-making processes and reduces development time, particularly in industries where feature prioritization is crucial. These findings underscore the potential of the integrated approach to enhance product development efficiency and feature prioritization, with suitability varying based on industry characteristics.

Simultaneously enhancing the flow properties and mechanical strength of polyphenylene sulfide via in‐mold ultrasonic‐assisted injection molding

AbstractInjection molding is widely used for making intricate plastic products due to its precision, cost efficiency, and fast production. Industries like consumer electronics, aerospace, and medical, demand high mechanical properties from these parts. However, high‐performance polymers face challenges in flowability. Ultrasonic‐assisted injection molding offers a solution, but its impact on flowability and mechanical properties is not fully understood. We propose a novel injection mold design with in‐mold ultrasonic vibration to address this. By applying high‐frequency shear forces to the melt, it alters polymer chain alignment, enhancing flow and microstructure. Employing polyphenylene sulfide (PPS) resin as the primary material, this study systematically explores the impact of varying ultrasonic vibration durations and stages on melt filling behavior and product performance. Compared with conventional molds, ultrasonic‐assisted molds reduce melt viscosity by 38% and improve mechanical properties by 10.21%, with a 17.1% increase in crystallinity. This technology shows promise for improving flowability and mechanical properties in injection molding, with potential for wide industrial application.

Three-dimensional high-aspect-ratio microarray thick electrodes for high-rate hybrid supercapacitors

Hybrid supercapacitors (HSCs) with facile integration and high process compatibility are considered ideal power sources for portable consumer electronics. However, as a crucial component for storing energy, traditional thin-film electrodes exhibit low energy density. Although increasing the thickness of thin films can enhance the energy density of the electrodes, it gives rise to issues such as poor mechanical stability and long electron/ion transport pathways. Constructing a stable three-dimensional (3D) ordered thick electrode is considered the key to addressing the aforementioned contradictions. In this work, a manufacturing process combining lithography and chemical deposition techniques is developed to produce large-area and high-aspect-ratio 3D nickel ordered cylindrical array (NiOCA) current collectors. Positive electrodes loaded with nickel–cobalt bimetallic hydroxide (NiOCA/NiCo-LDH) are constructed by electrodeposition, and HSCs are assembled with NiOCA/nitrogen-doped porous carbon (NiOCA/NPC) as negative electrodes. The HSCs exhibits 55% capacity retention with the current density ranging from 2 to 50 mA cm−2. Moreover, it maintains 98.2% of the initial capacity after long-term cycling of 15,000 cycles at a current density of 10 mA cm−2. The manufacturing process demonstrates customizability and favorable repeatability. It is anticipated to provide innovative concepts for the large-scale production of 3D microarray thick electrodes for high-performance energy storage system.

Effects of soldering temperature and preheating temperature on the properties of Sn–Zn solder alloys using wave soldering

Purpose This study aims to address the problem of low-temperature wave soldering in industry production with Sn-9Zn-2.5 Bi-1.5In alloys and develop qualified process parameters. Sn–Zn eutectic alloys are lead-free solders applied in consumer electronics due to their low melting point, high strength, and low cost. In the electronic assembly industry, Sn–Zn eutectic alloys have great potential for use. Design/methodology/approach This paper explored developing and implementing process parameters for low-temperature wave soldering of Sn–Zn alloys (SN-9ZN-2.5BI-1.5 In). A two-factor, three-level design of the experiments experiment was designed to simulate various conditions parameters encountered in Sn–Zn soldering, developed the nitrogen protection device of waving soldering and proposed the optimal process parameters to realize mass production of low-temperature wave soldering on Sn–Zn alloys. Findings The Sn-9Zn-2.5 Bi-1.5In alloy can overcome the Zn oxidation problem, achieve low-temperature wave soldering and meet IPC standards, but requires the development of nitrogen protection devices and the optimization of a series of process parameters. The design experiment reveals that preheating temperature, soldering temperature and flux affect failure phenomena. Finally, combined with the process test results, an effective method to support mass production. Research limitations/implications In term of overcome Zn’s oxidation characteristics, anti-oxidation wave welding device needs to be studied. Various process parameters need to be developed to achieve a welding process with lower temperature than that of lead solder(Sn–Pb) and lead-free SAC(Sn-0.3Ag-0.7Cu). The process window of Sn–Zn series alloy (Sn-9Zn-2.5 Bi-1.5In alloy) is narrow. A more stringent quality control chart is required to make mass production. Practical implications In this research, the soldering temperature of Sn-9Zn-2.5 Bi-1.5In is 5 °C and 25 °C lower than Sn–Pb and Sn-0.3Ag-0.7Cu(SAC0307). To the best of the authors’ knowledge, this work was the first time to apply Sn–Zn solder alloy under actual production conditions on wave soldering, which was of great significance for the study of wave soldering of the same kind of solder alloy. Social implications Low-temperature wave soldering can supported green manufacturing widely, offering a new path to achieve carbon emissions for many factories and also combat to international climate change. Originality/value There are many research papers on Sn–Zn alloys, but methods of achieving low-temperature wave soldering to meet IPC standards are infrequent. Especially the process control method that can be mass-produced is more challenging. In addition, the metal storage is very high and the cost is relatively low, which is of great help to provide enterprise competitiveness and can also support the development of green manufacturing, which has a good role in promoting the broader development of the Sn–Zn series.

Multilingual non-intrusive binaural intelligibility prediction based on phone classification

Speech intelligibility (SI) prediction models are a valuable tool for the development of speech processing algorithms for hearing aids or consumer electronics. For the use in realistic environments it is desirable that the SI model is non-intrusive (does not require separate input of original and degraded speech, transcripts or a-priori knowledge about the signals) and does a binaural processing of the audio signals. Most of the existing SI models do not fulfill all of these criteria. In this study, we propose an SI model based on phone probabilities obtained from a deep neural net. The model comprises a binaural enhancement stage for prediction of the speech recognition threshold (SRT) in realistic acoustic scenes. In the first part of the study, SRT predictions in different spatial configurations are compared to the results from normal-hearing listeners. On average, our approach produces lower errors and higher correlations compared to three intrusive baseline models. In the second part, we explore if measures relevant in spatial hearing, i.e., the intelligibility level difference (ILD) and the binaural ILD (BILD), can be predicted with our modeling approach. We also investigate if a language mismatch between training and testing the model plays a role when predicting ILD and BILD. This point is especially important for low-resource languages, where not thousands of hours of language material are available for training. Binaural benefits are predicted by our model with an error of 1.5 dB. This is slightly higher than the error with a competitive baseline MBSTOI (1.1 dB), but does not require separate input of original and degraded speech. We also find that good binaural predictions can be obtained with models that are not specifically trained with the target language.

How Linn Products Ltd. Has Achieved Organizational Fitness in the Digital Age

Overview: Prior research has understood the process of digital transformation as shifts between digitization, digitalization, and digital transformation phases and has treated the digital transformation process tautologically. In the literature, digital transformation is understood as transforming the whole organization, including its business processes and value proposition. This understanding is problematic because it fails to capture the need for achieving alignment between the organization, the environment, and the strategy based on the finer details of an organization’s context. Achieving this fit is important in a VUCA environment characterized by volatility, uncertainty, complexity, and ambiguity. We conducted a single case study of Linn Products, a UK-based high-tech company renowned for designing and manufacturing high-end home entertainment systems, including digital streaming products, music players, and speakers. This award-winning firm has not only embraced digital transformation but also sustained its performance proactively amidst the dynamic shifts in the consumable electronics industry. Our interpretive longitudinal study spans 2007 to 2023 and incorporates insights from 23 in-depth interviews. Our research provides valuable guidance for practitioners seeking to build resilience and adaptability in an era of digital disruption and polycrisis. Practitioner Takeaways: In an era marked by polycrisis, organizations must cultivate a combination of threshold and core capabilities to attain organizational fitness amidst the challenges of a VUCA environment. To leverage digital transformation, companies must adopt a strategic approach, whether through providing digital support in business processes or opting for full digital participation. Success in digital transformations hinges on the harmonious alignment of strategy, an organization’s unique circumstances, and the dynamic conditions of the VUCA environment.

Preparation of high-performance multilayer circuits by controlling the diffusion of Ag in borosilicate glass-ceramics through the addition of SiO2

Due to its superior reliability and performance, thick film technology is extensively employed in various applications, including automotive electronics, consumer electronics, communication engineering, and aerospace. However, a challenge arises from the mutual diffusion during the electronic paste production process, which leads to a decline in insulation performance and hinders the production of three-dimensional equipment. Inspired by the principles of low-temperature co-fired ceramic technology, a multi-layered ceramic structure has been successfully bonded using thick film technology by introducing a borosilicate glass-ceramics layer. To further enhance the properties of this multi-layered ceramic structure, approximately 8 wt% SiO2 was incorporated into the borosilicate glass-ceramics. This addition effectively inhibits silver diffusion and reduces the diffusion depth within the glass-ceramic film. The key mechanisms behind this improvement are the increased activation energy for crystallization, resulting in a slower crystallization rate due to the addition of SiO2. Moreover, the multi-layered ceramic with a sandwich structure exhibits remarkable properties. Notably, the dielectric strength of this system has been significantly enhanced to 13.9 kV/mm, accompanied by a high adhesion strength of 790 N. These enhanced properties of the optimized system indicate promising characteristics for multi-layer circuit applications.

Digital-Twin-Assisted Skill Learning for 3C Assembly Tasks.

The utilization of robots in computer, communication, and consumer electronics (3C) assembly has the potential to significantly reduce labor costs and enhance assembly efficiency. However, many typical scenarios in 3C assembly, such as the assembly of flexible printed circuits (FPCs), involve complex manipulations with long-horizon steps and high-precision requirements that cannot be effectively accomplished through manual programming or conventional skill-learning methods. To address this challenge, this article proposes a learning-based framework for the acquisition of complex 3C assembly skills assisted by a multimodal digital-twin environment. First, we construct a fully equivalent digital-twin environment based on the real-world counterpart, equipped with visual, tactile force, and proprioception information, and then collect multimodal demonstration data using virtual reality (VR) devices. Next, we construct a skill knowledge base through multimodal skill parsing of demonstration data, resulting in primitive policy sequences for achieving 3C assembly tasks. Finally, we train primitive policies via a combination of curriculum learning, residual reinforcement learning, and domain randomization methods and transfer the learned skill from the digital-twin environment to the real-world environment. The experiments are conducted to verify the effectiveness of our proposed method.

An energy-saving joint resource allocation strategy for mobile edge computing

Mobile Cloud-Edge Collaboration (MCEC) views in the main of converting the site for user electronics. By naturally integrating mobile devices with cloud computing (CC) resources at the edge of the scheme, this mutual paradigm improves storage, processing, and communication capabilities. This cooperation increases the performance of user electronics, delivering users responsive and resource-efficient knowledge. Offloading in Mobile Cloud-Edge Collaboration (MCEC) is a strategic device that recovers computational efficiency and resource energy for mobile devices. By reasonably moving computation tasks from mobile devices to the edge or cloud servers, offloading declines the load on the limited processing and energy capabilities of mobile devices. This joint method influences the stable computing power and storage aptitude accessible in the cloud-edge structure, confirming that resource-intensive uses like complex data processing or machine learning (ML) tasks can be implemented professionally. Offloading not only increases the receptiveness and performance of mobile users but also contributes to energy conservation, extending the battery time of mobile devices. This study proposes an African Vultures Optimizer algorithm-based Offloading Strategy for Mobile Cloud-Edge Collaboration (AVOAOS-MCEC) approach for consumer electronics. The AVOAOS-MCEC technique is based on the nature of AVOA is a new nature-based system, which is inspired by the unusual behavior of African vultures in foraging and navigation. In addition, the AVOAOS-MCEC technique designs a task offloading process to reduce the total energy utilization with the fulfillment of capacity and delay requirements. The experimental validation of the AVOAOS-MCEC method is verified utilizing distinct measures. An extensive comparison study stated that the AVOAOS-MCEC technique outperforms the other models in terms of several performance measures.